// This file is merged from the forces.cpp and social_forces_baseline.cpp
// The goal is that we from now on only have one file as our baseline
// This should make it more easy in the future to implement optimizations
// Our naming convention from now on is: variant_<variant_name>.cpp (Please make sure to keep that consistent, otherwise our Makefile wont work anymore)

#include "../headers/board.hpp"
#include "../headers/constants.hpp"
#include "../headers/forces.hpp"
#include "../headers/pedestrian.hpp"
#include "../headers/social_force.hpp"
#include "../headers/vector2d.hpp"

#include <algorithm>
#include <cmath>
#include <fstream>
#include <iostream>
#include <math.h> // keep — forces code uses unqualified sqrt/exp
#include <random>
#include <tuple>
#include <vector>

#define SEED 42
#define BOARD_WIDTH 10.0
#define BOARD_LENGTH 50.0
#define WALL_EPS 1e-3
#define BOARD_WIDTH_MINUS_WALL_EPS 9.999

#ifndef RANDOM_SEED
#define RANDOM_SEED 0
#endif

#ifndef BENCHMARK
#define BENCHMARK 0
#endif

using namespace std;

// forces.cpp

// Flop count: 3 ADDs, 7 MULTs (no SQRT, no branch)
// Branchless + sqrt-free: cos_phi < 0 so f.norm()*cos_phi <= 0.
//   - If dot >= 0: condition is trivially true.
//   - If dot < 0: both sides non-positive; square them safely:
//     dot^2 <= f.dot(f) * cos_phi^2.
// Bitwise | (not ||) keeps both sides evaluated and avoids a branch.
double w(Vector2d e, Vector2d f) {

  double dot = e.x * f.x + e.y * f.y;
  double f_dot_f = f.x * f.x + f.y * f.y;
  double cond = (dot >= 0.0) | (dot * dot <= f_dot_f * cos_phi_sq);
  return c_neg_times_dt + cond * (dt_neg - c_neg_times_dt);
}

// Flop count: 2 ADDs, 4 MULTs
Vector2d acceleration_term_dt(const Pedestrian &alpha) {

  // # 2 MULTs
  Vector2d desired_velocity = alpha.e * alpha.desired_v0;

  Vector2d actual_velocity = alpha.curr_vel;

  // # 2 ADDs, 2 MULTs
  Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv;

  return f_alpha_0_dt;
}

// Flop count: 12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP
Vector2d pedestrian_repulsion_dt(const Pedestrian &alpha, const Pedestrian &beta) {

  // # 2 ADDs
  // $\vec{r}_{\alpha\beta}$
  Vector2d r_ab = alpha.curr_pos - beta.curr_pos;

  // # 1 ADD, 2 MULTs, 1 SQRT
  // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$
  double r_ab_norm = r_ab.norm();
  r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS; // Avoid division by zero

  // $v_\beta \Delta t \vec{e}_\beta$
  Vector2d s_b_e = beta.s_b_e;

  // # 2 ADDs
  // $\vec{r}_{\alpha\beta} - \vec{v}_{\beta}\varDelta_{t}\vec{e}_{\beta}$
  Vector2d r_ab2 = r_ab - s_b_e;

  // # 1 ADD, 2 MULTs, 1 SQRT
  // $\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
  double r_ab2_norm = r_ab2.norm();
  r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS; // Avoid division by zero

  // # 1 ADD
  // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
  double r_ab_norm_sum = r_ab_norm + r_ab2_norm;

  // # 1 ADD, 1 MULTs, 1 SQRT
  //
  // $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
  //
  double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - beta.s_b_sq;
  two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ; // Avoid sqrt of negative and avoid division by zero
  double two_b = sqrt(two_b_sq);

  // # 5 MULTs, 1 DIV, 1 EXP
  //
  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
  //
  double scalar_mult_common = (r_ab_norm_sum / (two_b * r_ab_norm * r_ab2_norm)) * (NEG_V0_AB_div_TWO_SIGMA * exp(two_b * NEG_TWO_SIGMA_INV));

  // # 1 MULT
  //
  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}$
  //
  double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;

  // # 1 MULT
  //
  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
  //
  double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;

  // # 2 ADDs, 4 MULTs
  //
  // $\vec{-f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{-V^0_{\alpha \beta}}{2\sigma}e^{-b / \sigma} 2\grad_{\vec{r}_{\alpha \beta}}b $
  //
  //
  //          $= \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} -
  //          v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
  //
  //
  //
  // $2\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)(\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{2b}$
  //
  //
  //       $= \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
  //
  //
  Vector2d f_alpha_beta_neg = r_ab * scalar_mult_r_ab + r_ab2 * scalar_mult_r_ab2;

  // # 2 ADDs, 7 MULTs, 1 SQRT
  //
  // $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha \beta}).dt = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta})).\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta}).dt$
  //
  Vector2d f_alpha_beta_dt = f_alpha_beta_neg * w(alpha.e, f_alpha_beta_neg);

  return f_alpha_beta_dt;
}

// Flop count: 10 ADDs, 18 MULTs, 1 DIVs, 2 SQRTs, 1 EXP
Vector2d obstacle_repulsion_dt(const Pedestrian &alpha, const Obstacle &B) {

  // # 7 ADDs, 5 MULTs
  // $\vec{r}_{\alpha B}$
  Vector2d r_aB = alpha.curr_pos - B.find_nearest_obstacle_point(alpha.curr_pos);

  // # 1 ADD, 2 MULTs, 1 SQRT
  // $\Arrowvert \vec{r}_{\alpha B} \Arrowvert$
  double r_aB_norm = r_aB.norm();
  r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS; // Avoid division by zero

  // # 2 MULTs, 1 DIV, 1 EXP
  //
  // $\frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R}$
  //
  double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;

  // # 2 MULTs
  //
  // $-\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = \frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R} \frac{\vec{r}_{\alpha B}}{\Arrowvert \vec{r}_{\alpha B} \Arrowvert}$
  //
  Vector2d f_alpha_B_neg = r_aB * scalar_mult;

  // # 2 ADDs, 7 MULTs, 1 SQRT
  //
  // $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha B}).dt = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B}(\vec{r}_{\alpha B})).\vec{F}_{\alpha B}(\vec{r}_{\alpha B}).dt$
  //
  Vector2d f_alpha_B_dt = f_alpha_B_neg * w(alpha.e, f_alpha_B_neg);

  return f_alpha_B_dt;
}

// social_force_baseline.cpp

Pedestrian *init_pedestrians(int n_p) {
#if RANDOM_SEED
  random_device rd;  // Platform-provided random seed source.
  mt19937 gen(rd()); // Non-reproducible initialization.
#else
  mt19937 gen(SEED); // Reproducible initialization.
#endif

  normal_distribution<double> velocity_gen(v0_mean, v0_std_dev);
  uniform_real_distribution<double> y_gen(0.3, BOARD_WIDTH - 0.3);
  uniform_real_distribution<double> x_gen_left(2.0, BOARD_LENGTH / 2.0);
  uniform_real_distribution<double> x_gen_right(BOARD_LENGTH / 2.0, BOARD_LENGTH - 2.0);

  Pedestrian *P = new Pedestrian[n_p];

  int n_p_half = n_p / 2;

  // Group of pedestrians walking from left to right.
  for (int i = 0; i < n_p_half; i++) {
    double x = x_gen_left(gen);
    double y = y_gen(gen);

    Vector2d start_pos = Vector2d(x, y);
    Vector2d destination = Vector2d(BOARD_LENGTH + 30.0, y);

    Vector2d init_e = destination - start_pos;
    double init_e_norm = init_e.norm();
    Vector2d init_e_unit = init_e / init_e_norm;

    // Reject near-zero desired speeds to avoid pedestrians that barely move,
    // and reject negative speeds because they would reverse the intended
    // walking direction.
    double desired_v0;
    do {
      desired_v0 = velocity_gen(gen);
    } while (desired_v0 <= 0.1);

    double v_max = 1.3 * desired_v0;

    P[i] = Pedestrian(WHITE, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
  }

  // Group of pedestrians walking from right to left.
  for (int i = n_p_half; i < n_p; i++) {
    double x = x_gen_right(gen);
    double y = y_gen(gen);

    Vector2d start_pos = Vector2d(x, y);
    Vector2d destination = Vector2d(-30.0, y);

    Vector2d init_e = destination - start_pos;
    double init_e_norm = init_e.norm();
    Vector2d init_e_unit = init_e / init_e_norm;

    // Reject near-zero desired speeds to avoid pedestrians that barely move,
    // and reject negative speeds because they would reverse the intended
    // walking direction.
    double desired_v0;
    do {
      desired_v0 = velocity_gen(gen);
    } while (desired_v0 <= 0.1);

    double v_max = 1.3 * desired_v0;

    P[i] = Pedestrian(BLACK, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
  }

  return P;
}

Board init_board_without_obstacles() { return Board(BOARD_LENGTH, BOARD_WIDTH, 0, nullptr); }

int run_simulation(Pedestrian *pedestrians, Board &board, int n_p, int n_iter, const char *dump_path) {

#if BENCHMARK
  // Avoid compiler unused parameter warning
  (void)dump_path;
#endif

#if !BENCHMARK
  const int dump_interval = 1;

  // Each sample stores: time step, pedestrian id, x, y, e.x, e.y, color.
  vector<tuple<int, int, double, double, double, double, int>> samples;

  if (dump_path) {
    samples.reserve(static_cast<size_t>(n_iter / dump_interval + 1) * n_p);

    // Store the initial state before the first simulation step.
    for (int i = 0; i < n_p; i++) {
      Pedestrian &p = pedestrians[i];
      samples.emplace_back(0, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
    }
  }
#endif

  for (int t = 0; t < n_iter; t++) {
    // Pass 1: Compute all forces from the current state.
    for (int i = 0; i < n_p; i++) {
      Pedestrian &p = pedestrians[i];

      // # 2 ADDs, 4 MULTs
      //
      // $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
      //
      Vector2d f_alpha_dt = acceleration_term_dt(p);

      // # (N-1) * (12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP)
      //   + (N-1) * 2 ADDs
      //
      // $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha},\vec{r}_{\alpha} - \vec{r}_{\beta})$
      //
      // for (int j = 0; j < n_p; j++) {
      //  if (i == j)
      //    continue;
      //
      //  f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
      //}
      for (int j = 0; j < i; j++) {
        f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
      }
      for (int j = i + 1; j < n_p; j++) {
        f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
      }
      // # board.n_obstacles * (10 ADDs, 18 MULTs, 1 DIV, 2 SQRTs, 1 EXP)
      //   + board.n_obstacles * 2 ADDs
      //
      // $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
      //
      for (int k = 0; k < board.n_obstacles; k++) {
        f_alpha_dt += obstacle_repulsion_dt(p, board.obstacles[k]);
      }

      // # 2 ADDs
      //
      //
      // $\frac{d\vec{w}_\alpha}{dt} = \vec{F}_{\alpha}(t)$
      //
      p.w += f_alpha_dt;
    }

    // Pass 2: Update pedestrian states after all forces have been computed.
    // # (7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p
    for (int i = 0; i < n_p; i++) {
      Pedestrian &p = pedestrians[i];

      // # 1 ADD, 2 MULTs, 1 SQRT
      double w_norm = p.w.norm();

      double w_norm2 = w_norm > EPS ? w_norm : EPS; // Avoid division by zero.

      // # 1 DIV
      double correction_factor = std::min(1.0, p.v_max / w_norm2);

      // # 2 MULTs
      p.curr_vel = p.w * correction_factor;

      // # 2 ADDs, 2 MULTs
      //
      // $\vec{r}_i^{t+1} = \vec{r}_i^t + \vec{v}_i^t dt$
      //
      p.curr_pos = p.curr_pos + p.curr_vel * dt;

      // When a pedestrian reaches the end of the corridor, teleport them back
      // to the opposite entrance while keeping the same y-coordinate. This
      // maintains a constant pedestrian flow and makes lane formation easier to
      // observe.
      if (p.color == WHITE && p.curr_pos.x > (BOARD_LENGTH - 1.5)) {
        p.curr_pos.x = 1.0;
        p.destination.y = p.curr_pos.y;
        p.w = Vector2d(p.desired_v0, 0.0);
        p.curr_vel = p.w;
      }
      if (p.color == BLACK && p.curr_pos.x < 1.5) {
        p.curr_pos.x = BOARD_LENGTH - 1.0;
        p.destination.y = p.curr_pos.y;
        p.w = Vector2d(-p.desired_v0, 0.0);
        p.curr_vel = p.w;
      }

      // Keep pedestrians inside the board boundaries.
      p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);

      // # 2 ADDs
      Vector2d e = p.destination - p.curr_pos;

      // # 1 ADD, 2 MULTs, 1 DIV, 1 SQRT
      double e_norm = e.norm();
      double e_norm2 = std::fmax(e_norm, EPS); // Avoid division by zero.
      double e_norm_inv = 1.0 / e_norm2;

      // # 2 MULTs
      p.e = e * e_norm_inv;

      // # 1 ADD, 2 MULTs
      double velocity_norm_sq = p.curr_vel.dot(p.curr_vel);

      // # 1 SQRT
      double velocity_norm = std::sqrt(velocity_norm_sq);

      // # 1 MULT
      p.s_b_sq = velocity_norm_sq * delta_t_sq;

      // # 3 MULTs
      p.s_b_e = p.e * (velocity_norm * delta_t);

#if !BENCHMARK
      // Store the current pedestrian state for visualization.
      if (dump_path && (t % dump_interval == 0)) {
        samples.emplace_back(t + 1, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
      }
#endif
    }
  }

#if !BENCHMARK
  // Export recorded simulation states to CSV.
  if (dump_path) {
    ofstream out(dump_path);
    out << "time,id,x,y,p.e.x,p.e.y,color\n";

    for (auto &[t, id, x, y, e_x, e_y, c] : samples) {
      out << t << ',' << id << ',' << x << ',' << y << ',' << e_x << ',' << e_y << ',' << c << '\n';
    }
  }
#endif

  return 0;
}